Instructions to use Shularp/testjpth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shularp/testjpth with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Shularp/testjpth") model = AutoModelForSeq2SeqLM.from_pretrained("Shularp/testjpth", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: cc-by-nc-4.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: testjpth | |
| results: [] | |
| language: | |
| - ja | |
| - th | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # testjpth | |
| This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset. | |
| ## Model description | |
| This is test version to translate Japanese to Thai. I use NLLB for this model. | |
| ## Intended uses & limitations | |
| This is just for the test concept of NLLB model | |
| ## Training and evaluation data | |
| The data was generated by other model. The dataset was split by intention to use in order to make the model understand some technical term. | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Framework versions | |
| - Transformers 4.30.2 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.13.1 | |
| - Tokenizers 0.13.3 |